Real-time Parameter Estimation Research Articles

Event-triggered lifted robust MPC stabilization control for space telescope on dual-rate actuated rigid spacecraft systems

This study addresses the pose stabilization control problem for enhancing a space telescope mounted on a spacecraft system under a dual-rate actuated setup. An input-lifting approach is utilized to manage the dual rates in the control components, specifically the orbital space telescope and its loading platform. To counteract external spatial perturbations and spacecraft system uncertainties, an integrated control scheme is proposed, combining real-time model parameter estimation, active compensation control, and event-triggered lifted robust model predictive control (ET-LRMPC). The event-triggered mechanism in the proposed algorithm minimizes computational resource consumption while maintaining effective spacecraft control. Numerical simulations demonstrate that the proposed control scheme achieves favorable results under persistent external perturbations and model uncertainties. In terms of the overshoot, the algorithm proposed reduces the control effect by up to 87.1% in the spatial displacement of the payload and 98.6% in the Euler attitude angle compared to the conventional control algorithm. For the control of the base, the amount of overshoots with respect to the conventional control in spatial displacement and Euler attitude angle is reduced by 49% and 97.1%.

Real-time estimation of blood oxygenation parameters from human foot sole during leg elevation: A preliminary study with diffuse reflectance spectroscopy

This study investigated the applicability of diffuse reflectance spectroscopy (DRS) for real-time and non-invasive measurement of blood oxygenation parameters (BOPs) such as reduced hemoglobin (RHb), oxyhemoglobin (HbO2), and oxygen saturation (SO2) from human foot sole during leg elevation. Seventeen (17) healthy male subjects aged between 21 to 39 years were included in this study. Diffuse reflectance spectra were recorded from measurement sites namely the 5th metatarsal, ball of great joint, calcaneum, and great toe of the human foot sole w.r.t. leg elevation angles such as 00, 150, 300, 450, and 600, respectively. The localized BOPs were derived from the recorded spectra. In addition, blood hemodynamic parameters (BHPs) such as heart rate (HR), SO2, perfusion index (PI), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were also measured for each elevating angle. To study and assess the changes in BOPs and BHPs w.r.t. leg elevation, a One-way ANOVA test followed by a Tukey HSD post-hoc test was performed. We observed a statistically significant increase in RHb (p < 0.001) and a decrease in HbO2 (p < 0.001) after 45° of leg elevation, however, there was no statistically significant difference in SO2 (p = 0.74) and HR (p = 0.84) for each measurement site w.r.t. leg elevation, respectively. Furthermore, PI (p < 0.01), ankle SBP (p < 0.001) and DBP (p < 0.001) were decreased w.r.t. leg elevation. The obtained results are in agreement with the literature. The preliminary results suggest that DRS has the potential for real-time estimation of BOPs from the local sites of healthy human foot soles during leg elevation. Thus, it opens the possibility of DRS to monitor and evaluate the diagnosis and treatment of ischemia and edema during leg elevation of patients through BOPs measurement.

Sample-by-sample Power Quality Disturbance classification based on Sliding Window Recursive Discrete Fourier Transform

Power Quality Disturbances (PQDs) are caused by large-scale grid connections of nonlinear loads and Distributed Generations (DG). They affect the electrical and electronics equipment performance and may cause serious accidents and economic losses. The classification of PQDs becomes a key issue for end-users in order to enhance the Power Quality (PQ) and comprises an important step in monitoring the Electric Power System (EPS). Accurate PQDs classification is crucial for improving the quality of power supply, monitoring the condition of power equipment, and mitigating power grid issues. In the literature, several methods for detecting and classifying PQDs have been proposed. However, these methods consume a significant amount of processing time due to the detection and sample storage steps required to classify each disturbance class. In this work, we propose a method for classifying PQDs on a sample-by-sample basis. The innovation of this method lies in its ability to classify PQDs for each individual sample, thereby eliminating the need for window-based (batch) processing. To do so, it is applied the Sliding Window Recursive Discrete Fourier Transform (SWRDFT) to real-time estimation of PQ parameters. For the classification stage, decision tree, Artificial Neural Networks (ANN), Ensemble and the Naive Bayes classifiers were employed. The proposed method using Ensemble achieved the most satisfactory results for six different types of PQDs (oscillatory transients, harmonics, notches, sags, spikes and swells), in which oscillatory transients and spikes were identified in the first monitored samples (sample 2 and 11 respectively), sags were identified in the 14th sample counting from the beginning of the disturbance, notches were captured from the 35th sample, and harmonics and swells were identified after the 113th sample.

Real-time parameter estimation for two-qubit systems based on hybrid control

Real-time parameter estimation for two-qubit systems based on hybrid control

Real-time estimation of vehicle inertia parameters based on Kalman-bucy filter

Vehicle inertial parameters such as mass and moments of inertia are required for most vehicle dynamic control systems. Due to the wide range variation of these parameters during vehicle operation, accurate estimation of their values in real-time plays an important role in improving the efficiency of vehicle control systems. In this article, the vehicle sprung mass and moments of inertia are estimated in real-time based on a Kalman-Bucy filter algorithm designed for a spatial vibration model of a two-axle truck. This proposed method requires measuring only the vertical, roll, and pitch velocity of the sprung mass and, therefore can reduce the sensor cost significantly. The simulation results for a random roughness road profile according to ISO 8608 class C with step variations in sprung mass and moments of inertia showed that the designed estimator rejected the process and measurement noises and tracked the real vehicle parameters effectively with acceptable errors

Near-real-time multiparametric seismic and visual monitoring of explosive activity at Sabancaya volcano, Peru

This study presents the development of a multiparametric system that utilizes artificial intelligence techniques to identify and analyze volcanic explosions in near real-time. The study analyzed 1343 explosions recorded between 2019 and 2021, along with seismic, meteorological, and visible image data from the Sabancaya volcano. Deep learning algorithms like the U-Net convolutional neural network were used to segment and measure volcanic plumes in images, while boosting-based machine learning ensembles were used to classify seismic events related to ash plumes. The findings demonstrate that these approaches effectively handle large amounts of data generated during seismic and eruptive crises. The U-Net network achieved precise segmentation of volcanic plumes with over 98% accuracy and the ability to generalize to new data. The CatBoost classifier achieved an average accuracy of 94.5% in classifying seismic events. These approaches enable the real-time estimation of eruptive parameters without human intervention, contributing to the development of early warning systems for volcanic hazards. In conclusion, this study highlights the feasibility of using seismic signals and images to detect and characterize volcanic explosions in near real-time, making a significant contribution to the field of volcanic monitoring.

Optimizing Electric Vehicle Performance, Range and Parameter Estimation through NEDC Urban and Suburban Analysis using MATLAB

This research paper focuses on electric vehicle (EV) driving range and parameter estimation, with an emphasis on analyzing the New European Driving Cycle (NEDC) urban and suburban drive cycles. By examining these drive cycles, we obtain critical data on the moments when an electric vehicle transitions between constant velocity, acceleration, and deceleration phases, which significantly affect power consumption and driving range. We investigate various techniques for parameter estimation, including battery capacity, energy consumption rates, and powertrain efficiency, essential for improving EV performance and providing realistic range expectations. Empirical experiments in diverse driving conditions contribute valuable data to refine our understanding of EV driving range and performance in different scenarios. The research underscores the role of predictive modeling, data analytics, and advanced technologies in real-time parameter estimation, offering precise and convenient range predictions to enhance user confidence.

Real-time estimation of barycenter position for fuel cell distributed drive electric tractor based on derived UKF

ABSTRACT The FCDET is considered as a promising and advanced agricultural machinery due to its convenience and controllability. To enhance the control performance of the FCDET unit dynamic system, understanding the accurate position of the center of gravity (CoG) of the tractor unit in longitudinal motion and the operating environment is essential. In this paper, we proposed a real-time estimation method of CoG position of longitudinal motion for FCDET plowing and transportation conditions. CoG height and longitudinal axis distance distribution coefficients are used as state estimation variables, and the data obtained from displacement and velocity sensors are used as state observation variables, establishing a discrete system state space model and proving the observability. The proposed Derived UKF algorithm introduces the Sage-Husa adaptive noise equation within the Square-Root UKF framework to address the non-positive definite matrix problem. The research shows that the precise CoG longitudinal position effectively reflects changes in operational pavement load. The Derived UKF algorithm exhibits high state estimation accuracy in plowing and transportation conditions, with mean absolute errors of 14.2% and 5.0% of the state amplitude, respectively. This study supports the practical application of real-time state parameter estimation algorithms in controlling advanced agricultural machinery distributed drive systems.

Deep learning for parameter estimation of supermassive binary black holes with simulated LISA data

Previous studies have demonstrated that deep learning is an effective approach for processing gravitational wave (GW) data obtained from ground-based detectors, as it can enhance the efficiency of data processing and offer great potential for real-time parameter estimation. In this paper, we explore three different deep learning architectures (MCD, TFP, and VI) for inferring the chirp mass (Mc) and the luminosity distance (DL) of supermassive binary black holes (SMBBHs) using simulated data from the Laser Interferometer Space Antenna (LISA). We train the neural networks with the simulated data and evaluate their performance on predicting the parameters (Mc,DL). The results show that more than 97.5% of the true values fall within the 3σ confidence interval of the predicted values with the optimal network. To verify the accuracy of the network on parameter estimation, we also calculate the estimation error of the parameters using the Fisher Information Matrix (FIM) with the same simulated data. By comparing the root-mean-square error (RMSE) between deep learning and FIM, we find that the two methods are comparable, which implies that deep learning can be reliable for GW parameter estimation.

An Interpretable Digital Twin for Self-Aware Industrial Machines.

This paper presents a proposed three-step methodology designed to enhance the performance and efficiency of industrial systems by integrating Digital Twins with particle swarm optimization (PSO) algorithms while prioritizing interpretability. Digital Twins are becoming increasingly prevalent due to their capability to offer a comprehensive virtual representation of physical systems, thus facilitating detailed simulations and optimizations. Concurrently, PSO has demonstrated its effectiveness for real-time parameter estimation, especially in identifying both standard and unknown components that influence the dynamics of a system. Our methodology, as exemplified through DC Motor and Hydraulic Actuator simulations, underscores the potential of Digital Twins to augment the self-awareness of industrial machines. The results indicate that our approach can proficiently optimize system parameters in real-time and unveil previously unknown components, thereby enhancing the adaptive capacities of the Digital Twin. While the reliance on accurate data to develop Digital Twin models is a notable consideration, the proposed methodology serves as a promising framework for advancing the efficiency of industrial applications. It further extends its relevance to fault detection and system control. Central to our approach is the emphasis on interpretability, ensuring a more transparent understanding and effective usability of such systems.

Recovery With Incomplete Knowledge: Fundamental Bounds on Real-Time Quantum Memories

The recovery of fragile quantum states from decoherence is the basis of building a quantum memory, with applications ranging from quantum communications to quantum computing. Many recovery techniques, such as quantum error correction, rely on the apriori knowledge of the environment noise parameters to achieve their best performance. However, such parameters are likely to drift in time in the context of implementing long-time quantum memories. This necessitates using a "spectator" system, which estimates the noise parameter in real-time, then feed-forwards the outcome to the recovery protocol as a classical side-information. The memory qubits and the spectator system hence comprise the building blocks for a real-time (i.e. drift-adapting) quantum memory. In this article, I consider spectator-based (incomplete knowledge) recovery protocols as a real-time parameter estimation problem (generally with nuisance parameters present), followed by the application of the "best-guess" recovery map to the memory qubits, as informed by the estimation outcome. I present information-theoretic and metrological bounds on the performance of this protocol, quantified by the diamond distance between the "best-guess" recovery and optimal recovery outcomes, thereby identifying the cost of adaptation in real-time quantum memories. Finally, I provide fundamental bounds for multi-cycle recovery in the form of recurrence inequalities. The latter suggests that incomplete knowledge of the noise could be an advantage, as errors from various cycles can cohere. These results are illustrated for the approximate [4,1] code of the amplitude-damping channel and relations to various fields are discussed.

Application of adaptive global terminal sliding mode control based on limited error in high-speed train

High-precision tracking control is very important in high-speed train (HST) operation. This study proposes an adaptive global terminal sliding mode control scheme based on limited error (AEG-SMC) for trajectory tracking of HST. AEG-SMC is firstly proposed for the nonlinear dynamic system to improve the accuracy of tracking error and solve the problem of parameter uncertainty in the model. Then, AEG-SMC uses an adaptive method to approximate the uncertain parameters in the model to obtain real-time parameter estimation, and an error index function is given to realise fast and high-precision tracking of the system by global terminal sliding mode control (GSMC). AEG-SMC is able to design the required error indicators before conducting control, thereby ensuring the high-quality response and stability of the system. Finally, the simulation results show that the speed tracking error and control input of HST with AEG-SMC are also the smallest among the mentioned methods.

Fast Gravitational-wave Parameter Estimation without Compromises

We present a lightweight, flexible, and high-performance framework for inferring the properties of gravitational-wave events. By combining likelihood heterodyning, automatically differentiable, and accelerator-compatible waveforms, and gradient-based Markov Chain Monte Carlo sampling enhanced by normalizing flows, we achieve full Bayesian parameter estimation for real events like GW150914 and GW170817 within a minute of sampling time. Our framework does not require pretraining or explicit reparameterizations and can be generalized to handle higher dimensional problems. We present the details of our implementation and discuss trade-offs and future developments in the context of other proposed strategies for real-time parameter estimation. Our code for running the analysis is publicly available on GitHub at https://github.com/kazewong/jim.

Guidelines for designing a digital twin for Li-ion battery: A reference methodology

The integration of digital technologies is causing a significant change in the energy sector. These innovations have transformed traditional energy grids into intelligent grids. As a result, the digital replica of Battery Energy Storage Systems (BESS) has become one of the most crucial components in the energy sector. Digital twin technology enables the seamless integration of BESS into intelligent grids and offers numerous benefits, such as easy identification and prediction of faults, real-time system monitoring, optimization, temperature regulation, and estimation of parameters. As a result, the overall performance of BESS is improved by the digital twin technology. Consequently, this paper discusses the general guidelines that must be followed to develop a digital twin for a Li-ion BESS successfully. The main function is to define how to design a digital twin able to optimize the system and facilitate early and predictive fault detection and diagnosis.

Predicting optimal membrane hydration and ohmic losses in operating fuel cells with machine learning

The absence of proper membrane hydration in membrane electrode assembly (MEA)-based electrochemical energy conversion technologies can lead to disastrous effects, such as reactant crossover, material degradation, and cell failure. In this work, we are the first to apply machine learning to predict if the optimal membrane hydration has been reached in polymer electrolyte membrane (PEM) fuel cells. We evaluate the ability of machine and deep learning algorithms to predict the high frequency resistance (HFR) and whether the optimal hydration current density (OHCD) has been reached via parameters measured from electrochemical impedance spectroscopy (EIS). We accurately predict the HFR using a long short-term memory recurrent neural network model (3.11% mean absolute percentage error and over 0.95 R2). We also accurately predict OHCD with precision and recall values over 98% using deep learning and a k-nearest neighbours model. Furthermore, our models provide a powerful tool for real-time parameter estimation and control systems to monitor and improve the performance of PEM fuel cells and other electrochemical energy devices, such as water and carbon dioxide electrolyzers.

Identification and control of Maglev system using fractional and integer order PID controller

System identification techniques have proved to be the most effective methodologies for the modeling highly non-linear and system. For the purpose of real-time parameter estimation of a Maglev system, a Teaching Learning Based Optimization (TLBO) for updating the weights of Functional Link Artificial Neural Network (FLANN) model is proposed and implemented in this research. Moreover, we proposed a one &amp; two-Degree of Freedom (one-DOF &amp; two-DOF) Fractional Order PID (FOPID) controller, where the parameters are optimized by using the Teaching Learning Based Optimization (TLBO) and the recently proposed Black Widow Optimization (BWO) algorithm. To investigate the robustness of the proposed controller, a pulse signal disturbance is added at equal intervals of the output of the identified model of the Maglev system. It is found that the suggested two-DOF FOPID controller with TLBO performs better than its counterpart in terms of both in time domain specifications (i.e., maximum overshoot = 1.2648%, settling time = 1.3884 sec and rise time = 0.8685 sec) and in robustness analysis (i.e., system is sufficiently robust, because the infinity norms of the sensitivity and the complementary sensitivity functions of the system are less than two). The TLBO algorithm has performed better for both identification and optimization of controller parameter due to very less number of algorithmic parameter is as compared to other algorithm.

A novel physics-informed framework for real-time adaptive modal parameters estimation of offshore structures

Singular value decomposition provides a rigorous mathematical foundation for these monitoring methods, such as modal analysis, damage detection, etc. As substantial limitations including computational efficiency and adaptive capabilities have been identified in the matrix process, data-driven algorithms with enhanced efficiency and accuracy to adapt the rapid development of information and technologies have been highly required in offshore structures monitoring. To meet this demand, a novel physics-informed frame work enabling the real-time adaptive monitoring has been proposed in this paper. The working principle of the developed framework has been represented by the smart conversion of physics-informed modal identification into the optimal process of fast and accurate solving an eigensystem governed by differential equations via recurrent neural network. The ingenious design of the proposed framework has complied with the rule of singular value decomposition used for modal identification and therefore, learning capabilities of the physics-informed framework have been remarkably enhanced by successfully addressing two bottleneck problems including proper initialized input values and the optimal time increment of the developed recurrent neural network. Both numerical simulations and the field-data based study of a mono-pile offshore wind turbine structure have been presented to examine the superior performance of the proposed framework. Results have shown that the proposed framework has the ability to adaptively identify modal parameters with a higher level of computational efficiency as compared with traditional methods. Furthermore, the computational advantage of the develop framework has demonstrated the potential to integrate with sensor networks and edge computing for smart monitoring and maintenance in various engineering subjects.

Adaptive Dynamic Voltage Support Scheme for Fault Ride-Through Operation of a Microgrid

This paper proposes a control strategy for enabling voltage fault ride-through (VFRT) operation with adaptive dynamic voltage support (DVS) scheme using the energy storage in a microgrid. Firstly, the condition for maximum DVS from the microgrid is presented and its dependency on the parameters of the equivalent network seen by the microgrid is elucidated. Considering the impediments associated with the real-time parameter estimation during the short-term voltage disturbances, reference current signals based on the extreme voltage tracking algorithm are proposed in this paper to achieve an adaptive DVS irrespective of the varying system conditions. Secondly, a control strategy is proposed to enable the energy storage-based VFRT operation of a microgrid. Thirdly, a unique methodology of sizing the energy storage for VFRT operation is presented considering the grid code requirements, equivalent network characteristics, and the maximum current rating of the power electronic interface with the grid. The proposed control strategy is validated through real-time digital simulation case studies for low-voltage, high-voltage, and multiple-fault scenarios. The scalability of the proposed approach is illustrated using the CIGRE distribution test network.

Real-time nonlinear parameter estimation and tracking control of unmanned aerial vehicles in closed-loop

The real-time unknown parameter estimation and adaptive tracking control problems are investigated in this paper for a six degrees of freedom (6-DOF) of under-actuated quadrotor unmanned aerial vehicle (UAV). A virtual proportional derivative (PD) controller is designed to maintain the translational dynamics. Two adaptive schemes are developed to handle the attitude dynamics of the UAV with several unknown parameters. In the beginning, a classical adaptive scheme (CAS) using the certainty equivalence principle is proposed and designed. The idea is to design a controller for an ideal situation by assuming the unknown parameters were known. Then the unknown parameters are replaced by their estimation. A theoretical analysis is provided to ensure the trajectory tracking of the adaptive controller. However, an inherent drawback of this scheme is that there is no guarantee for the estimated parameters to converge to the actual values. To address this issue, a new adaptive scheme (NAS) is developed as the next step by adding a continuously differentiable function to the control structure. The proposed technique guarantees handling of the parametric uncertainties with an appropriate design manifold. A rigorous analytical proof, numerical simulation analyses, and experimental validation are presented to show the effectiveness of the proposed control design.

Semantic Learning for Analysis of Overlapping LPI Radar Signals

The increasingly complex radio environment may cause the received low probability of intercept (LPI) radar signals to overlap in time–frequency domains. Analyzing overlapping LPI radar signals requires identifying the modulation type and estimating the parameters of each component. Prior research performs overlapping signal analysis as a multistage task, where each stage is designed to perform a part of the task. The multistage system will increase the calculation burden and cannot be optimized as a whole. Instead, this article proposes a novel framework for analyzing overlapping signals in a single stage. Specifically, we develop a joint semantic learning deep convolutional neural network (JSLCNN) that jointly learns three tasks, i.e., feature restoration, modulation classification, and parameter regression. Since the whole cognitive pipeline is a single network, it can be optimized end-to-end directly on cognitive performance. To verify the validity of the proposed JSLCNN, numerous comparative experiments are carried out in terms of modulation recognition and parameter estimation of overlapping signals. Experimental results demonstrate that the JSLCNN has desirable extensibility for identifying unseen signal combinations and robustness against unknown jamming. Furthermore, we show that the JSLCNN outperforms other existing approaches in generic real-time parameter estimation for LPI radar signals.